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Use of Environmental Parameters to Model Pathogenic Vibrios in Chesapeake Bay

E. A. Urquhart1*,B. F. Zaitchik1,S. D. Guikema2,B. J. Haley3,E. Taviani3,A. Chen3,M. E. Brown4,A. Huq3 and R. R. Colwell5

  1. Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
  2. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
  3. Maryland Pathogen Research Institute, Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
  4. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  5. Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Studies (UMIACS), University of Maryland, College Park, MD 20742, USA

*Corresponding author. Tel: +603-862-2250 Fax: +603-862-1101 Email:


Although the transportation sector is a major contributor to urban air pollution and global climate change due to its substantial energy consumptions, previous studies for evacuation practices in this sector seldom took environmental consequences into account. As an attempt in event-related evacuation planning under uncertainty, this study proposed an emission-mitigation-oriented fuzzy evacuation management (emoFEM) model. Comprehensive considerations over system efficiency, environmental protection, economic cost and resource availability were incorporated within a general modeling formulation to facilitate evacuation management in a systematic and compromise manner. Vague and ambiguous information embedded within evacuation problems could be quantified and directly communicated into the optimization process, greatly improving conventional tools for evacuation management under uncertainty. The proposed emoFEM model was then applied to a hypothetic but representative case. Useful solutions were generated, which could help identify timely, safe and cost-effective evacuation schemes without significant disturbances over normal municipal traffic and environmental quality. The advantages of emoFEM were further revealed through comparing its solutions with those from its deterministic counterpart.

Keywords: quantitative colony bot hybridization, hybrid modeling, classification, regression, generalized additive model, random forest model

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